Understanding the Electronic and Structural Effects in ORR Intermediate Binding on Anion-Substituted Zirconia Surfaces.

For oxygen reduction reaction (ORR), the surface adsorption energies of O and OH* intermediates are key descriptors for catalytic activity. In this work, we investigate anion-substituted zirconia catalyst surfaces and determine that adsorption energies of O and OH* intermediates is governed by both structural and electronic effects. When the adsorption energies are not influenced by the structural effects of the catalyst surface, they exhibit a linear correlation with integrated crystal orbital Hamiltonian population (ICOHP) of the adsorbate-surface bond. The influence of structural effects, due to the re-optimisation slab geometry after adsorption of intermediate species, leads to stronger adsorption of intermediates. Our calculations show that there is a change in the bond order to accommodate the incoming adsorbate species which leads to stronger adsorption when both structural and electronic effects influence the adsorption phenomena. The insights into the catalyst-adsorbate interactions can guide the design of future ORR catalysts.

Artificial Intelligence Applied to Battery Research: Hype or Reality?

This is a critical review of artificial intelligence/machine learning (AI/ML) methods applied to battery research. It aims at providing a comprehensive, authoritative, and critical, yet easily understandable, review of general interest to the battery community. It addresses the concepts, approaches, tools, outcomes, and challenges of using AI/ML as an accelerator for the design and optimization of the next generation of batteries─a current hot topic. It intends to create both accessibility of these tools to the chemistry and electrochemical energy sciences communities and completeness in terms of the different battery R&D aspects covered.

Cation-Coordinated Inner-Sphere CO2 Electroreduction at Au-Water Interfaces.

Electrochemical CO2 reduction reaction (CO2RR) is a promising technology for the clean energy economy. Numerous efforts have been devoted to enhancing the mechanistic understanding of CO2RR from both experimental and theoretical studies. Electrolyte ions are critical for the CO2RR; however, the role of alkali metal cations is highly controversial, and a complete free energy diagram of CO2RR at Au-water interfaces is still missing. Here, we provide a systematic mechanism study toward CO2RR via ab initio molecular dynamics simulations integrated with the slow-growth sampling (SG-AIMD) method. By using the SG-AIMD approach, we demonstrate that CO2RR is facile at the inner-sphere interface in the presence of K cations, which promote the CO2 activation with the free energy barrier of only 0.66 eV. Furthermore, the competitive hydrogen evolution reaction (HER) is inhibited by the interfacial cations with the induced kinetic blockage effect, where the rate-limiting Volmer step shows a much higher energy barrier (1.27 eV). Eventually, a comprehensive free energy diagram including both kinetics and thermodynamics of the CO2RR to CO and the HER at the electrochemical interface is derived, which illustrates the critical role of cations on the overall performance of CO2 electroreduction by facilitating CO2 adsorption while suppressing the hydrogen evolution at the same time.

Oxidation and de-alloying of PtMn particle models: a computational investigation.

We present a computational study of the energetics and mechanisms of oxidation of Pt-Mn systems. We use slab models and simulate the oxidation process over the most stable (111) facet at a given Pt2Mn composition to make the problem computationally affordable, and combine Density-Functional Theory (DFT) with neural network potentials and metadynamics simulations to accelerate the mechanistic search. We find, first, that Mn has a strong tendency to alloy with Pt. This tendency is optimally realized when Pt and Mn are mixed in the bulk, but, at a composition in which the Mn content is high enough such as for Pt2Mn, Mn atoms will also be found in the surface outmost layer. These surface Mn atoms can dissociate O2 and generate MnOx species, transforming the surface-alloyed Mn atoms into MnOx surface oxide structures supported on a metallic framework in which one or more vacancy sites are simultaneously created. The thus-formed vacancies promote the successive steps of the oxidation process: the vacancy sites can be filled by surface oxygen atoms, which can then interact with Mn atoms in deeper layers, or subsurface Mn atoms can intercalate into interstitial sites. Both these steps facilitate the extraction of further bulk Mn atoms into MnOx oxide surface structures, and thus the progress of the oxidation process, with typical rate-determining energy barriers in the range 0.9-1.0 eV.

Structural and electronic properties of double wall MoSTe nanotubes.

Janus nanotubes originating from rolling up asymmetric dichalcogenide monolayers have shown unique properties compared to their 2D and 3D counterparts. Most of the work on Janus nanotubes is focused on single-wall (SW) tubes. In this work, we have investigated the structural and electronic properties of double wall (DW) MoSTe nanotubes using Density Functional Theory (DFT). The most stable DW, corresponding to a minimum of the strain energy, is formed by combining 16- and 24-unit cells for the inner and outer tubes. This DW configuration shows a slightly smaller inner diameter than the SW tube, which was formed by 18-unit cells due to the intra-wall interaction. The investigation of the band gaps of 2D structures under strain and SW/DW nanotubes revealed that the curvature of the nanotube and the strain induced when forming the tube are the two primary factors enabling the band gap tuning. Moreover, we found that the band gaps of the DW MoSTe tubes close, compared to the SWs, generating tubes with a metallic-like behavior. This property makes DW MoSTe nanotubes promising for electrochemical applications.

Graph neural network interatomic potential ensembles with calibrated aleatoric and epistemic uncertainty on energy and forces.

Inexpensive machine learning (ML) potentials are increasingly being used to speed up structural optimization and molecular dynamics simulations of materials by iteratively predicting and applying interatomic forces. In these settings, it is crucial to detect when predictions are unreliable to avoid wrong or misleading results. Here, we present a complete framework for training and recalibrating graph neural network ensemble models to produce accurate predictions of energy and forces with calibrated uncertainty estimates. The proposed method considers both epistemic and aleatoric uncertainty and the total uncertainties are recalibrated post hoc using a nonlinear scaling function to achieve good calibration on previously unseen data, without loss of predictive accuracy. The method is demonstrated and evaluated on two challenging, publicly available datasets, ANI-1x (Smith et al. J. Chem. Phys., 2018, 148, 241733.) and Transition1x (Schreiner et al. Sci. Data, 2022, 9, 779.), both containing diverse conformations far from equilibrium. A detailed analysis of the predictive performance and uncertainty calibration is provided. In all experiments, the proposed method achieved low prediction error and good uncertainty calibration, with predicted uncertainty correlating with expected error, on energy and forces. To the best of our knowledge, the method presented in this paper is the first to consider a complete framework for obtaining calibrated epistemic and aleatoric uncertainty predictions on both energy and forces in ML potentials.

Cheap Turns Superior: A Linear Regression-Based Correction Method to Reaction Energy from the DFT.

Workflows to predict chemical reaction networks based on density functional theory (DFT) are prone to systematic errors in reaction energy due to the extensive use of cheap DFT exchange-correlation functionals to limit computational cost. Recently, machine learning-based models are increasingly applied to mitigate this problem. However, machine learning models require systems similar to trained data, and the models often perform poorly for out-of-distribution systems. Here, we present a simple bond-based correction method that improves the accuracy of DFT-derived reaction energies. It is based on linear regression, and the correction terms for each bond are derived from reactions among the QM9 data set. We demonstrate the effectiveness of this method with three DFT functionals in three different rungs of Jacob's ladder. The simple correction method is effective for all rungs but especially so for the cheapest PBE functional. Finally, we applied the correction method to a few reactions with molecules significantly different from those in the QM9 data set that was used to fit the linear regression model. Once corrected by this method, we found that the DFT reaction energies for such out-of-distribution reactions are within 0.05 eV of the G4MP2 method.

Structure and energetics of liquid water-hydroxyl layers on Pt(111).

The interactions between liquid water and hydroxyl species on Pt(111) surfaces have been intensely investigated due to their importance to fuel cell electrocatalysis. Here we present a molecular dynamics study of their structure and energetics using an ensemble of neural network potentials, which allow us to obtain unprecedented statistical sampling. We first study the energetics of hydroxyl formation, where we find a near-linear adsorption energy profile, which exhibits a soft and gradual increase in the differential adsorption energy at high hydroxyl coverages. This is strikingly different from the predictions of the conventional bilayer model, which displays a kink at 1/3ML OH coverage indicating a sizeable jump in differential adsorption energy, but within the statistical uncertainty of previously reported ab initio molecular dynamics studies. We then analyze the structure of the interface, where we provide evidence for the water-OH/Pt(111) interface being hydrophobic at high hydroxyl coverages. We furthermore explain the observed adsorption energetics by analyzing the hydrogen bonding in the water-hydroxyl adlayers, where we argue that the increase in differential adsorption energy at high OH coverage can be explained by a reduction in the number of hydrogen bonds from the adsorbed water molecules to the hydroxyls.

Transition-Metal-Free Barium Hydride Mediates Dinitrogen Fixation and Ammonia Synthesis.

Transition-metal-mediated dinitrogen fixation has been intensively investigated. The employment of main group elements for this vital reaction has recently sparked interest because of new dinitrogen reaction chemistry. We report ammonia synthesis via a chemical looping process mediated by a transition-metal-free barium hydride (BaH2 ). Experimental and computational studies reveal that the introduction of hydrogen vacancies is essential for creating multiple coordinatively unsaturated Ba sites for N2 activation. The adjacent lattice hydridic hydrogen (H- ) then undergoes both reductive elimination and reductive protonation to convert N2 to NHx . The ammonia production rate supports this hydride-vacancy mechanism via a chemical looping route that far exceeds that of a catalytic process. The BaH2 -mediated chemical looping process has prospects in future technologies for ammonia synthesis using transition-metal-free materials.

Heterogeneity in susceptibility dictates the order of epidemic models.

The fundamental models of epidemiology describe the progression of an infectious disease through a population using compartmentalized differential equations, but typically do not incorporate population-level heterogeneity in infection susceptibility. Here we combine a generalized analytical framework of contagion with computational models of epidemic dynamics to show that variation strongly influences the rate of infection, while the infection process simultaneously sculpts the susceptibility distribution. These joint dynamics influence the force of infection and are, in turn, influenced by the shape of the initial variability. We find that certain susceptibility distributions (the exponential and the gamma) are unchanged through the course of the outbreak, and lead naturally to power-law behavior in the force of infection; other distributions are often sculpted towards these "eigen-distributions" through the process of contagion. The power-law behavior fundamentally alters predictions of the long-term infection rate, and suggests that first-order epidemic models that are parameterized in the exponential-like phase may systematically and significantly over-estimate the final severity of the outbreak. In summary, our study suggests the need to examine the shape of susceptibility in natural populations as part of efforts to improve prediction models and to prioritize interventions that leverage heterogeneity to mitigate against spread.

CO2 activation at Au(110)-water interfaces: An ab initio molecular dynamics study.

The electrochemical reduction of CO2 into valuable chemicals under mild conditions has become a promising technology for energy storage and conversion in the past few years, receiving much attention from theoretical researchers investigating the reaction mechanisms. However, most of the previous simulations are related to the key intermediates of *COOH and *CO using the computational hydrogen electrode approach under vacuum conditions, and the details of the CO2 activation are usually ignored due to the model simplicity. Here, we study the CO2 activation at the Au-water interfaces by considering the dynamics of an explicit water solvent, where both regular ab initio molecular dynamics and constrained ab initio molecular dynamics simulations are carried out to explore the CO2 adsorption/desorption reactions from the atomic level. By introducing K+ cations into Au(110)-water interfacial models, an electrochemical environment under reducing potentials is constructed, where the reaction free energy (0.26 eV) and activation energy (0.61 eV) are obtained for CO2 adsorption based on the thermodynamic integration. Moreover, the Bader charge analysis demonstrates that CO2 adsorption is activated by the first-electron transfer, forming the adsorbed CO2 - anion initiating the overall catalytic reaction.

Is the water/Pt(111) interface ordered at room temperature?

The structure of the water/Pt(111) interface has been a subject of debate over the past decades. Here, we report the results of a room temperature molecular dynamics study based on neural network potentials, which allow us to access long time scale simulations while retaining ab initio accuracy. We find that the water/Pt(111) interface is characterized by a double layer composed of a primary, strongly bound adsorption layer with a coverage of ∼0.15 ML, which is coupled to a secondary, weakly bound adsorption layer with a coverage of ∼0.58 ML. By studying the order of the primary adsorption layer, we find that there is an effective repulsion between the adsorbed water molecules, which gives rise to a dynamically changing, semi-ordered interfacial structure, where the water molecules in the primary adsorption layer are distributed homogeneously across the interface, forming frequent hydrogen bonds to water molecules in the secondary adsorption layer. We further show that these conclusions are beyond the time scales accessible to ab initio molecular dynamics.

The effect of CO2 contamination in rechargeable non-aqueous sodium-air batteries.

Metal-air batteries have higher theoretical specific energies than existing rechargeable batteries including Li-ion batteries. Among metal-air batteries, the Na-O2 battery has gained much attention due to its low discharge/charge overpotentials (∼100 mV) at relatively high current densities (0.2 mA/cm2), high electrical energy efficiency (90%), high theoretical energy density, and low cost. However, there is no information reported regarding the effect of CO2 contamination in non-aqueous Na-air batteries. Density functional theory has, here, been applied to study the effect of low concentrations of CO2 contamination on NaO2 and Na2O2 growth/depletion reaction pathways and overpotentials. This was done on step surfaces of discharge products in non-aqueous Na-air batteries. Adsorption energies of CO2 at various nucleation sites for both step surfaces were determined, and results revealed that CO2 preferentially binds at the step valley sites of (001) NaO2 and 11¯00 Na2O2 surfaces with binding energies of -0.65 eV and -2.67 eV, respectively. CO2 blocks the step nucleation site and influences the reaction pathways and overpotentials due to carbonate formation. The discharge electrochemical overpotential increases remarkably from 0.14 V to 0.30 V and from 0.69 V to 1.26 V for NaO2 and Na2O2 surfaces, respectively. CO2 contamination is thus drastically impeding the growth/depletion mechanism pathways and increases the overpotentials of the surface reaction mechanism, hampering the performance of the battery. Avoiding CO2 contamination from intake of gas and electrolyte decomposition is thus critical in development of Na-air batteries.

Multi-Electron Reactions Enabled by Anion-Based Redox Chemistry for High-Energy Multivalent Rechargeable Batteries.

The development of multivalent metal (such as Mg and Ca) based battery systems is hindered by lack of suitable cathode chemistry that shows reversible multi-electron redox reactions. Cationic redox centres in the classical cathodes can only afford stepwise single-electron transfer, which are not ideal for multivalent-ion storage. The charge imbalance during multivalent ion insertion might lead to an additional kinetic barrier for ion mobility. Therefore, multivalent battery cathodes only exhibit slope-like voltage profiles with insertion/extraction redox of less than one electron. Taking VS4 as a model material, reversible two-electron redox with cationic-anionic contributions is verified in both rechargeable Mg batteries (RMBs) and rechargeable Ca batteries (RCBs). The corresponding cells exhibit high capacities of >300 mAh g-1 at a current density of 100 mA g-1 in both RMBs and RCBs, resulting in a high energy density of >300 Wh kg-1 for RMBs and >500 Wh kg-1 for RCBs. Mechanistic studies reveal a unique redox activity mainly at anionic sulfides moieties and fast Mg2+ ion diffusion kinetics enabled by the soft structure and flexible electron configuration of VS4 .

The influence of silica surface groups on the Li-ion conductivity of LiBH4/SiO2 nanocomposites.

Lithium borohydride is a promising lithium ion conductor for all-solid-state batteries. However, the compound only exhibits high ionic conductivity at elevated temperatures, typically above 110 °C. It was shown that the addition of oxides such as silica or alumina increases the room temperature ionic conductivity by 3 orders of magnitude. The origin of this remarkable effect is not yet well understood. Here, we investigate the influence of oxide surface groups on the ionic conductivity of LiBH4/SiO2 nanocomposites. We systematically varied the density and nature of the surface groups of mesoporous silica by heat treatment at different temperatures, or surface functionalization, and subsequently prepared LiBH4/SiO2 nanocomposites by melt infiltration. The ionic conductivity is strongly influenced by the heat treatment temperature, hence the density of the free surface silanol groups. Replacing some of the silanol groups with hydrophobic surface groups resulted in an order of magnitude reduction of the room temperature ionic conductivity, suggesting that their presence is crucial to obtain high ionic conductivity in the nanocomposites. This systematic study and insight provide a basis for further exploration of the impact of surface groups, and for the rational design of novel solid-state nanocomposite electrolytes via interface engineering.

Combinatorial selection of a two-dimensional 3d-TM-tetracyanoquinodimethane (TM-TCNQ) monolayer as a high-activity nanocatalyst for CO oxidation.

The CO oxidation reaction on single 3d-transition metal catalytic sites in experimentally realized tetracyanoquinodimethane (TM-TCNQ) monolayers (TM = Sc-Zn) is systematically investigated by means of first-principles calculations. Considering the stabilities, adsorption characteristics and thermodynamics of all the ten candidates (Sc-Zn), Sc-TCNQ is found to display the lowest activation energies and yield the highest catalytic activity for room temperature CO oxidation. Exploring the Langmuir-Hinshelwood (LH) and Eley-Rideal (ER) mechanisms, we find that the rate-limiting step of CO oxidation catalyzed by Sc-TCNQ (CO + O2* → OOCO*) can follow the LH mechanism with free energy barriers as low as 0.73 eV at 300 K. The second step of CO + O* → CO2 can occur with rather small energy barriers via either LH or ER mechanisms. The high activity of Sc-TCNQ can be attributed to its unique structural and electronic features by possessing high stability, optimum adsorption energies with adsorbates, and fast reaction kinetics. These results have significant implications for the synthesis of two-dimensional single atom catalysis for CO oxidation with low-cost and high activity at low temperature.

A density functional theory study of the carbon-coating effects on lithium iron borate battery electrodes.

Lithium iron borate (LiFeBO3) is a promising cathode material due to its high theoretical specific capacity, inexpensive components and small volume change during operation. Yet, challenges related to severe air- and moisture-induced degradation have prompted the utilization of a protective coating on the electrode which also improves the electronic conductivity. However, not much is known about the preferential geometries of the coating as well as how these coating-electrode interfaces influence the lithium diffusion between the coating and the electrode. Here, we therefore present a density functional theory (DFT) study of the anchoring configurations of carbon coating on the LiFeBO3 electrode and its implications on the interfacial lithium diffusion. Due to large barriers associated with Li-ion diffusion through a parallel-oriented pristine graphene coating on the FeBO3 and LiFeBO3 electrode surfaces, large structural defects in the graphene coating are required for fast Li-ion diffusion. However, such defects are expected to exist only in small concentrations due to their high formation energies. Alternative coating geometries were therefore investigated, and the configuration in which the coating layers were anchored normal to the electrode surface at B and O atoms was found to be most stable. Nudged elastic band (NEB) calculations of the lithium diffusion barriers across the interface between the optimally oriented coating layers and the electrode show no kinetic limitations for lithium extraction and insertion. Additionally, this graphite-coating configuration showed partial blocking of electrode-degrading species.

Decoupling strain and ligand effects in ternary nanoparticles for improved ORR electrocatalysis.

Ternary Pt-Au-M (M = 3d transition metal) nanoparticles show reduced OH adsorption energies and improved activity for the oxygen reduction reaction (ORR) compared to pure Pt nanoparticles, as obtained by density functional theory. The strain and ligand effects in nanoparticles are decoupled and correlated with the extended Pt(111) surface for benchmarking. The ternary metal in the core allows for tuning the catalytic activity through strain effects. Pt-Au-M for M = Cr, Mn, Co, Cu, Zn nanoparticles are of particular interest as they exhibit an optimal contribution of strain, ligand effects and stability. Good agreement is found with experimental studies showing increased activity of Pt-Au-Fe/Ni nanoparticles, and mid to late 3d transition metals are predicted to exhibit enhance activity and stability with respect to pure Pt nanoparticles.

First principles investigation of the activity of thin film Pt, Pd and Au surface alloys for oxygen reduction.

Further advances in fuel cell technologies are hampered by kinetic limitations associated with the sluggish cathodic oxygen reduction reaction. We have investigated a range of different formulations of binary and ternary Pt, Pd and Au thin films as electrocatalysts for oxygen reduction. The most active binary thin films are near-surface alloys of Pt with subsurface Pd and certain PdAu and PtAu thin films with surface and/or subsurface Au. The most active ternary thin films are with pure metal Pt or Pd skins with some degree of Au in the surface and/or subsurface layer and the near-surface alloys of Au with mixed Pt-Pd skins. The activity of the binary and ternary catalysts is explained through weakening of the OH binding energy caused by solute elements. However, given the low alloy formation energies it may be difficult to tune and retain the composition under operating conditions. This is particularly challenging for alloys containing Au due to a high propensity of Au to segregate to the surface. We also show that once Au is on the surface it will diffuse to defect sites, explaining why small amounts of Au retard dissolution of Pt nanoparticles. For the PtPd thin films there is no pronounced driving force for surface segregation, diffusion to defects or surface self-assembling. On the basis of stability and activity analysis we conclude that the near surface alloy of Pd in Pt and some PdAu binary and PtPdAu ternary thin films with a controlled amount of Au are the best catalysts for oxygen reduction.

A DFT-based genetic algorithm search for AuCu nanoalloy electrocatalysts for CO2 reduction.

Using a DFT-based genetic algorithm (GA) approach, we have determined the most stable structure and stoichiometry of a 309-atom icosahedral AuCu nanoalloy, for potential use as an electrocatalyst for CO2 reduction. The identified core-shell nano-particle consists of a copper core interspersed with gold atoms having only copper neighbors and a gold surface with a few copper atoms in the terraces. We also present an adsorbate-dependent correction scheme, which enables an accurate determination of adsorption energies using a computationally fast, localized LCAO-basis set. These show that it is possible to use the LCAO mode to obtain a realistic estimate of the molecular chemisorption energy for systems where the computation in normal grid mode is not computationally feasible. These corrections are employed when calculating adsorption energies on the Cu, Au and most stable mixed particles. This shows that the mixed Cu135@Au174 core-shell nanoalloy has a similar adsorption energy, for the most favorable site, as a pure gold nano-particle. Cu, however, has the effect of stabilizing the icosahedral structure because Au particles are easily distorted when adding adsorbates.